Coronary artery disease (CAD) is the leading cause of the death in the US and, in concert with the epidemic of obesity and diabetes, is becoming the leading cause of death in many developing countries. The genetic predilection of CAD is well-established. Despite this, the genetics of CAD remain largely unknown. Given the complex nature of CAD, evaluation of the disease with more comprehensive analytical tools may provide needed insights into biological pathways converging on this heterogeneous phenotype. Many of the commonly accepted risk factors for CAD are metabolic. Metabolomics, the study of small-molecule metabolites, is an emerging discipline that may be particularly useful for understanding metabolic imbalances and for diagnosis of disease. Using these granular metabolic phenotypes, which reflect biological responses to exogenous and endogenous inputs, in a genetic screen may provide more a more powerful method for uncovering molecular mechanisms of cardiovascular disease. We have previously shown a novel finding of high heritabilities of metabolomic profiles in families burdened with early-onset CAD, suggesting a genetic basis to these metabolite profiles. Furthermore, we have shown that these metabolomic profiles strongly, and independently, discriminate individuals with CAD from those without, and predict risk of future cardiovascular events. Therefore, we propose to perform metabolic quantitative trait loci (mQTL) mapping in a large, well-phenotyped cardiovascular cohort using genomewide association (GWAS) and targeted, quantitative metabolic profiling, with the goal of elucidating the underlying genetic architecture of metabolic traits predisposing to CAD. We hypothesize that metabolomic profiling in this cohort will identify novel phenotypes underlying CAD pathophysiology, and that mQTL mapping will identify novel genes for CAD risk mediated through metabolic pathways. The specific aims of this proposal are to: (1) perform targeted, quantitative metabolic profiling in a well-phenotyped cardiovascular cohort of 1000 individuals; (2) perform genomewide association (GWAS) in the same cardiovascular cohort of 1000 individuals and perform genetic mapping to identify metabolic quantitative trait loci (mQTLs); (3) replicate GWAS findings in silico and in independent familial and nonfamilial cardiovascular cohorts; and (4) resequence candidate loci identified from Aims 2 and 3 to identify novel genetic variants. This proposal has the potential for having a significant impact on a major public health problem that has a very strong heritable component that is poorly